Neutrino flavor evolution in dense astrophysical environments is inherently nonlinear and sensitive to many-body (MB) quantum effects beyond the mean-field approximation. Existing MB studies are constrained by small system sizes, closed boundaries, and highly idealized symmetry assumptions. We present a unified tensor-network framework that enables simulations of inhomogeneous and anisotropic flavor evolution under conditions relevant to core-collapse supernovae and neutron-star mergers. Within this framework, we examine the effects of inhomogeneity, boundary conditions, and convergence with resolution for multiple neutrino distributions, allowing direct comparison of these setups under one consistent formulation. In our simulations, many-body systems equilibrate earlier than their mean-field counterparts while approaching similar final flavor states. Enlarging the interaction region allows open boundaries to reproduce closed-system behavior, but only when the beams begin superimposed and interact continuously. By contrast, initially separated configurations develop entanglement more slowly, interact over longer times, and equilibrate to a flavor content that differs from that obtained from initially superimposed calculations.

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• Novae & supernovae
• Plasma instabilities
• Neutrinos
• Many-body techniques
• Tensor network methods